Application of Large Language Models.
With the introduction of Transformer [1]} style architectures, we have seen a diverse range of applications of large language models (LLMs) pre-trained on web-scale textual data. They have not only performed well on natural language processing tasks [2]}, [1]}, but the implicit knowledge learned by these models have shown to be effective for other unrelated tasks [4]}. LLMs has had a lot of success in vision-and-language tasks like Visual Question Answering (VQA) [5]}, [6]} and image captioning [7]}, [8]}, external knowledge-based question answering [9]}, [10]} and construction [11]}. They have also been shown to improve performance on Embodied AI tasks like vision-and-language navigation [12]}, [13]}, instruction following [14]}, and planning for embodied tasks [15]}, [16]}. In our work, we explore if language models can display common-sense knowledge of how humans prefer to tidy up their homes.
